© 2018 – Routledge (Supplementary (DRM-Free))
200 pages | 16 B/W Illus.
Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment. Well-known contributors provide empirical, theoretical, and practical perspectives on the current use and future potential of learning analytics for student learning and data-driven decision-making, ways to effectively evaluate and research learning analytics, integration of learning analytics into practice, organizational barriers and opportunities for harnessing Big Data to create and support use of these tools, and ethical considerations related to privacy and consent. Designed to give readers a practical and theoretical foundation in learning analytics and how data can support student success in higher education, this book is a valuable resource for scholars and administrators.
List of Tables
List of Figures
Chapter 1: Absorptive capacity and routines: Understanding barriers to learning analytics adoption in higher education
Chapter 2. Analytics in the field: Why locally grown continuous improvement systems are essential for effective data driven decision-making
Matthew T. Hora
Chapter 3: Big data, small data, and data shepherds
Jennifer DeBoer and Lori Breslow
Chapter 4: Evaluating scholarly teaching: A model and call for an evidence-based approach
Daniel L. Reinholz, Joel C. Corbo, Daniel J. Bernstein, and Noah D. Finkelstein
Chapter 5: Discipline-focused learning analytics approaches with users instead of for users
David B. Knight, Cory Brozina, Timothy J. Kinoshita, Brian J. Novoselich, Glenda D. Young, and Jacob R. Grohs
Chapter 6: Student consent in learning analytics: The devil in the details?
Paul Prinsloo and Sharon Slade
Chapter 7: Using learning analytics to improve student learning outcomes assessment in higher education: Potential, constraint, & possibility
Carrie Klein, and Richard M. Hess
Chapter 8: Data, data everywhere: Implications and considerations
Matthew D. Pistilli